# MAT 240 SNHU Week 8 Core Applications of Statistical Analysis Questions: Statistics Answers 2021

MAT 240 SNHU Week 8 Core Applications of Statistical Analysis Questions: Statistics Answers 2021

## MAT 240 SNHU Week 8 Core Applications of Statistical Analysis Questions: Statistics Answers 2021

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MAT 240 SNHU Week 8 Core Applications of Statistical Analysis Questions

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1
Predicting Sales based on Median Square Feet
Stacy Blaise
Southern New Hampshire University
Course
May 13,2021
2
Generating a Representative Sample
The objective is to determine whether the median square foot or the size of the property has any
impact on the selling price of the properties. For this purpose, real estate county data for the year
2019 has been selected. It includes information about the property region, county, median listing
price, median per square foot and median square feet. The data contained information about
different properties located in different counties.
The region selected for the study is the northeast region. A random sample of 30 data is selected
for the study. The data is shown below.
3
Descriptive Statistics
Median listing Price
Median Square Feet
\$192,268
1726.48
\$167,058
1736.60
\$83717.30
264.73
Mean
Median
Standard deviation
The average listing price of the randomly selected sample was \$192,268 (SD=83717.30). The
standard deviation is relatively high, indicating variation in listing price. Further,50% of the
randomly selected data had a price higher than \$!67058. The average size of the property was
found to be 1726.48 square feet (SD=264.73). Also,50% of the randomly selected data had a size
larger than 1736.60 square feet.
Analyze Sample
Comparing with Population
Table Showing Summary Statistics of the Population Data
Mean
Sample
Population
Median listing
Median Square
Median listing Median
Price
Feet
Price
Square Feet
\$192,268
1726.48
\$288,407
1944
4
Median
Standard deviation
\$167,058
83717.30
1736.60
264.73
\$256,936
\$163,986
1901
367
From the above comparison, the mean of the median and mean listing price of the sample is less
than that of the population. Further, the standard deviation was also found to be less than that of
the population. Similar is the case with the area where the mean, median and standard deviation
of the national market is higher than that of the randomly selected regional market.
Method to get Random sample
The region sample is created by first selecting all the data of the northeast regions. Now
each region was assigned a number using Excel with the formula =randbetween (1,100). It
indicates assigning number randomly of each subset from 1 to 100. The number assigned was
then sorted in increasing order meaning starting from 1 until the number assigned highest to the
sample. After sorting based on the increasing value of random values, the first 30 samples were
taken. In this way, random samples of 30 regions were obtained.
Scatterplot
A scatterplot of the median square feet and the median listing price is shown in the below figure.
Here, the independent variable is the size of the property (median square feet) and the dependent
variable is the median listing price in dollar.
5
Median listing price in \$
Scatter Plot
y = 238.89x – 220171
R² = 0.5707
\$500,000
\$450,000
\$400,000
\$350,000
\$300,000
\$250,000
\$200,000
\$150,000
\$100,000
\$50,000
\$0
0
500
1000
1500
2000
2500
3000
Median Square Feet
Observe Pattern
The scatter plot shows median square feet or the size of the property on the x-axis and the
price of the property (median listing price) on the y-axis. The independent variable or the median
square feet helps make predictions. Based on the size of the property, we can then estimate the
price of the house based on the linear regression. The scatter plot shows a positive trend line
indicating an increase in the size of the property will lead to an increase in the sales of the
property. The scatter plot shows a positive association meaning a larger property size has a
higher sales figure. The shape of the scatter plot shows a linear trend. A trend line with the
equation is shown in the above figure. The linear regression can be written as;
Sale=238.89*Area-220171
For the house of 1200 square foot, the sales price would be \$66497( 128.89*1200-220171).
The scatter plot has potential outlines (x=2398,y=459154). The outliers appeared as extreme
observation to the right. It appeared because of the maximum size of 2398 sq foot for the
randomly selected data.
6
Running Head: LA PETITE COSETTE MARKET RESEARCH
MKT 113: Final Project Part I Final Submission
Stacy Blaise
Southern New Hampshire University
1
LA PETITE COSETTE MARKET RESEARCH
2
La petite Cosette is a new pet food line tailored to cater to the nutritional needs of
both cats and dogs. La petite Cosette has a healthy, non-GMO, all-natural formulation. This
research will analyze the most suitable target market and marketing strategy that would be used
in the successful launch of La petite Cosette in the market.
The SWOT matrix of La petite Cosette will analyze its strengths, weaknesses,
opportunities, and threats (Malhotra et al., 2017). The main strength of La petite Cosette is its
high-quality ingredient formulation that is guaranteed to keep pets safe, healthy, and energized.
The product will incorporate a higher fiber content, improved freshness, and non-processed
meat, making it superior to other natural pet food lines in the market. Furthermore, the company
has a strong brand presence, both at a retail level and online spaces. The primary weakness of La
petite Cosette is its higher price point compared to other products in the market. Furthermore, the
company’s low social media presence may hinder marketing efforts. The pet’s food line’s main
opportunity is the increased prospect in health and nutritional food markets. More dog and cat
owners are becoming concerned about their health and nutritional status of their pets and are
willing to spend more to maintain this factor. Furthermore, there is an upward trend in dog and
cat ownership in the US, meaning more prospective customers for the product. Consequently, the
chief threat of La Petite Cosette is the increased competition in the organic pet food market. A
comprehensive SWOT analysis of the new pet food line provides a basis for creating a proper
marketing plan. In this case, the marketing strategy will emphasize the high nutritional content of
La petite Cosette. Market segmentation will focus on identifying high-earning individuals in an
urban area as its primary customers to counter its higher price point. The product takes full
advantage of the growing prospect in the organic pet food market.
LA PETITE COSETTE MARKET RESEARCH
3
For effective identification of the target market, demographic, psychographic, and
geographic information will be implemented for the market segmentation (Martin, 2011). La
petite Cosette would be marketed to single and familiar households living in the high-income
neighborhood of Aventura, Miami, FL. This neighborhood primarily comprises people of
Caucasian origin with a high income and high educational background. Currently, the estimated
median household income of Ventura stands at \$77,597. Based on the wealth and education of
the neighborhood, there is a higher likelihood of pet owners of being more willing to spend more
to obtain the best quality foods for their pets. Furthermore, due to the upscale and suburban
lifestyle of Aventura, residents usually spend their leisure time in outdoor activities, including
golfing, shopping, and taking walks in the beaches and parks. Therefore, the product will be
geared towards targeting individuals who are expected to have pet companions with them during
those activities.
The demographic, psychographic, and geographic segmentation of Aventura
residents plays a significant role in determining their needs and wants. In relation to dog owners,
the majority of highly educated, high-income earners are concerned about the quality of products
for their pets, regardless of the price. Nutritionally, pet owners of Aventura are on the market for
higher protein, higher fiber food products. Furthermore, there is a particular emphasis on
organic, ethically sourced materials in their pet’s food ingredients. Due to the busy lifestyle that
the target market leads, their primary want is product convenience. Therefore, the users need to
use and store the food product without any difficulties or time-consuming steps. Since the
consumers are high profile individuals of society, they would want to obtain their foods from a
highly reputable brand. Therefore, La petite Cosette is a Pet food product that will satisfy the
needs and wants of Aventura residents. The high-quality ingredients will satisfy the nutritional
LA PETITE COSETTE MARKET RESEARCH
4
needs of both dogs and cats. Furthermore, its non-GMO and all-natural factor addresses the
customers’ organic wants for their pets. Additionally, La Petite Cosette is a pet line associated
with a reputable brand.
Social media is the primary marketing channel for La Petite Cosette. Specifically,
social networks and the company’s website will be great avenues to advertise the product. Due to
their high income and higher education, the residents of Aventura will have a high propensity
towards social media. Therefore, the main aim would be to increase the social media presence of
the products in highly influential social networks. Facebook, Instagram, Tik Tok would be most
hashtags, will ensure that the target market receives the message. La petite Cosette would also
benefit from a highly functional company website with high-ranking pages. SEO strategies,
including keyword ranking, backlinking, and creating attractive landing pages, will increase
consumer awareness of the product (Tuten, 2020). To effectively reach the targeted markets,
highly specific keywords and hashtags for Aventura residents will be used. Social media
marketing has been used by companies such as Nike for its online presence, to grow its brand
authenticity, and engage in public relations. In terms of the 4P’s, La Petite Cosette is a highquality product that offers a nutritional and environmental advantage. Therefore, due to the
versatility of social media platforms, they can communicate the qualities of the products
effectively. In relation to price and promotion, social media provides the advantage of
customizing both the price and the target markets of the product. Therefore, marketing efforts
without families. Lastly, social media offers the advantage of the geographical targeting of
LA PETITE COSETTE MARKET RESEARCH
5
customers. Therefore, advertisements on all platforms would be directed towards the Aventura
neighborhood.
The main marketing strategy that would not be recommended for the launch of La
petite Cosette is through public relations. This includes activities such as press releases, media
interviews, sponsorships, conferences, and host events. These strategies would ineffective in the
promotion of La Petite Cosette since they focus on promoting the company, and not the specific
product. For example, press releases are not conducive for emphasizing the specific high-quality
features of La Petite Cosette. In relation to the 4P’s, the product the new La petite Cosette. The
promotion strategies will include press releases, conferences, sponsorships, and hosting events.
The geographical location of public relations would be Aventura, Miami. That said, the
promotion factor of public relations would be changed, then the strategy would be more likely to
succeed.
In conclusion, La petite Cosette is a new pet food line with high nutritional,
organic, all-natural formulation, specifically for cats and dogs. The main strength of the product
is its high ingredient formulation, while its main weakness is the higher price point as compared
to other products. Market segmentation involves targeting family and individual pet owners in
the high-income neighborhood of Aventura ,Miami. Due to the high-income level and
educational background of the target market, there is an emphasis on the quality of products over
its price. Therefore, the La Petite Cosette would be accepted easily in the market. The most
effective marketing strategy for the product line is social media platforms.
LA PETITE COSETTE MARKET RESEARCH
6
References
Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing research: An applied approach.
Pearson Education Limited.
Martin, G. (2011). The importance of marketing segmentation. American Journal of Business
Education (AJBE), 4(6), 15-18.
Tuten, T. L. (2020). Social media marketing. Sage.
Regional vs. National Housing Price Comparison Report
Report: Regional vs. National Housing Price Comparison
Stacy Blaise
Southern New Hampshire University
1
Regional vs. National Housing Price Comparison Report
2
Introduction
Purpose:
2 hypothesis tests and 1 confidence interval interpretation are needed to be done to determine
that region’s housing prices and square footage of homes are different from national values.
Sample:
2 samples are drawn randomly from pacific region data set. 1st sample is 100 housing prices and
the 2nd is 100 square footage data.
Questions and type of test:
One sample t test is done to test the claim that average regional housing price is greater than the
national housing price. It’s a right tailed test. One sample t test is also done for the second test, to
test the claim that average regional square footage of home is different from the national square
footage of homes. That’s a two tailed test.
1-Tail Test
Hypothesis
Population parameter: National housing price
Null hypothesis: Average regional housing price is same as the national housing price
Alternative hypothesis: Average regional housing price is greater than the national housing price
H0 : μ = 288407
H1 : μ > 288407
Regional vs. National Housing Price Comparison Report
3
Data analysis
Histogram
Housing price
40
35
Frequency
30
25
20
15
Frequency
10
5
0
Bin
Summary statistics
Mean
Standard Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
399310.8
15747.92
349950
450050
157479.2
2.48E+10
2.347826
1.467454
799475
198050
997525
39931077
100
Regional vs. National Housing Price Comparison Report
4
Quartiles
Q1
Q3
295412.5
450050
Summary of sample data
Center of the data set is measured by the median, it is 349950. Data spreading is the standard
deviation which is equal to 157479.2. We have a right skewed shape for housing price and can
be assumed to be normally distributed since sampling is also done as simple random sampling.
Sample size is also saying about the normally as it is greater than 30.
Hypothesis Test Calculations:
Test statistics
=
t=
̅ −
/√
399310.8 − 288407
157479.2/ √100
t = 7.042
P value
P value is calculated using excel function
=T.DIST.RT([test statistic], [degree of freedom])
P value = 0
Interpretation:
Calculated p value is less than the significance level 0.05.
Null hypothesis is rejected
We can conclude that regional housing price is greater than the national housing price
Regional vs. National Housing Price Comparison Report
5
2-Tail Test
Hypothesis
Population parameter: National square footage of homes
Null hypothesis: Average regional square footage of home is same as the national square footage
of homes
Alternative hypothesis: Average regional square footage of home is different from the national
square footage of homes
H0 : μ = 1944
H1 : μ ≠ 1944
Data Analysis:
Histogram
20
18
16
14
12
10
8
6
4
2
0
Frequency
1200
1300
1400
1500
1600
1700
1800
1900
2000
2100
2200
2300
2400
2500
2600
More
Frequency
Square foot
Bin
Regional vs. National Housing Price Comparison Report
6
Summary statistics
Mean
Standard Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
1904.036429
27.31287476
1828
2588
273.1287476
74599.31277
0.330794159
0.322346174
1324
1264
2588
190403.6429
100
Quartiles
Q1
Q2
1765.125
2079.875
Summary of sample data
Center of the data set is measured by the median 1828. Spread of data is measured by the
standard deviation which is equal to 273.13. Square footage data is approximately symmetric
according to the histogram and can be assumed to be normally distributed since sampling is
also done as simple random sampling with sample size which is greater than 30.
Hypothesis Test Calculations:
Test statistics
=
t=
̅ −
( /√ )
1904.04 − 1944
273.13/ √100
Regional vs. National Housing Price Comparison Report
t = −1.463
P value
P value is calculated using excel function
=T.DIST.RT([test statistic], [degree of freedom])
P value = 0.147
Interpretation:
Calculated p value is greater than the significance level 0.05.
Null hypothesis is not rejected
We do not have sufficient evidence to support the claim that average regional square footage of
home is different from the national square footage of homes.
Comparison of the Test Results:
Confidence interval equation
= ̅ ± (

)
tα/2 = t0.025,99 = 1.984
Substitute values
CI = 1904.04 ± 1.984
273.13
√100
CI = 1904.04 ± 54.19
= ( . , . )
95% confidence interval of average square footage is ( . , . )
7
Regional vs. National Housing Price Comparison Report
8
Interpretation
We are 95% confident that true mean of square footage of homes lies between 1849.85 and
1958.23.
Since the national average square footage 1944 lies between the calculated confidence
interval, null hypothesis is not rejected.
Final Conclusions
Summary
Two samples are created from pacific region population data. They are housing price sample and
square footage of homes. Tested the claim that Pacific regional average housing price is higher
that, that of for population. We concluded that the claim is accepted since the null hypothesis is
rejected according to test results. Then one sample t test was done to test the claim that average
regional square footage of home is different from the national square footage of homes. The
conclusion is average regional square footage of home is not different from the national square
footage of homes. This is confirmed by confidence interval interpretation.
As per the results of 2 hypothesis tests and 1 confidence interval interpretation, drawn
conclusions are acceptable.
Hypothesis Testing for Regional Real Estate Company
HypothesisTesting for Regional Real Estate Company
Stacy Blaise
Southern New Hampshire University
1
Hypothesis Testing for Regional Real Estate Company
2
Introduction
This real state data analysis is done to test the pacific region salesperson’s claim that the average
cost per square foot of his home sales is above the average cost per square foot in the Pacific
region. Sample of 1001 cost per square foot in the Pacific region is given for the analysis. As per
the claim, left tailed t test is done using the 0.05 significance level.
Setup
Population parameter
Mean of cost per square foot
Null hypothesis
H0 : Average cost per square foot in the pacific region is 275
Alternative hypothesis
H1 : Average cost per square foot in the pacific region is less than 275
This is a left tailed test
Significance level: α = 0.05
Data Analysis Preparations
Sample is the home sales of the Pacific region, sample size is 1001.
[Provide the descriptive statistics of the sample.]
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
264.016393
5.11263417
202.965842
206.165334
161.756506
26165.1672
4.50212972
2.08640264
Hypothesis Testing for Regional Real Estate Company
Range
Minimum
Maximum
Sum
Count
3
967.451596
103.832378
1071.28397
264280.409
1001
Histogram
600
Frequency
500
400
300
Frequency
200
100
0
200 300 400 500 600 700 800 900 1000 More
Bin
Histogram shows that the data set is right skewed. Median of the data set is 203 while the mean
is 264.02. Da…

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